On the Performance of Discretization and Trust-Region Methods for On-Board Convex Low-Thrust Trajectory Optimization

2022 ◽  
Author(s):  
Christian Hofmann ◽  
Andrea C. Morelli ◽  
Francesco Topputo
Keyword(s):  
Trajectory Optimization ◽  
Trust Region ◽  
Trust Region Methods ◽  
Low Thrust

Combining line search and trust-region methods forℓ1-minimization

2017 ◽  
Vol 95 (10) ◽  
pp. 1950-1972 ◽  
Author(s):  
Hamid Esmaeili ◽  
Majid Rostami ◽  
Morteza Kimiaei
Keyword(s):  
Line Search ◽  
Trust Region ◽  
Trust Region Methods

Solving nonlinear magnetic problems using newton trust region methods

2003 ◽  
Vol 39 (3) ◽  
pp. 1709-1712 ◽  
Author(s):  
H. Vande Sande ◽  
H. De Gersem ◽  
F. Henrotte ◽  
K. Hameyer
Keyword(s):  
Trust Region ◽  
Trust Region Methods

scholarly journals A Nonmonotone Trust Region Algorithm Based on the Average of the Successive Penalty Function Values for Nonlinear Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zhensheng Yu ◽  
Jinhong Yu
Keyword(s):  
Global Convergence ◽  
Penalty Function ◽  
Optimization Problems ◽  
Trust Region ◽  
Trust Region Methods ◽  
Constrained Optimization Problems ◽  
Trust Region Algorithm ◽  
Numerical Tests ◽  
Equality Constrained Optimization ◽  
Nonlinear Equality

We present a nonmonotone trust region algorithm for nonlinear equality constrained optimization problems. In our algorithm, we use the average of the successive penalty function values to rectify the ratio of predicted reduction and the actual reduction. Compared with the existing nonmonotone trust region methods, our method is independent of the nonmonotone parameter. We establish the global convergence of the proposed algorithm and give the numerical tests to show the efficiency of the algorithm.

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